Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = 'data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7fef405888d0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7feef005c9e8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = "inputs_real")
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name = "inputs_z")
    lr = tf.placeholder(tf.float32)
    return inputs_real, inputs_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/usr/lib/python3.5/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-6-7def13f5ee86>", line 22, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/gtx1080/Desktop/jim/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/gtx1080/Desktop/jim/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/gtx1080/Desktop/jim/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/gtx1080/Desktop/jim/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/gtx1080/.local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [39]:
def discriminator(images, reuse=False, alpha=0.01):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope("discriminator", reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        #x1 = tf.nn.dropout(x1, 0.7)
        x1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        #drop2 = tf.nn.dropout(x2, 0.5)
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        #drop3 = tf.nn.dropout(x3, 0.5)
        x3  = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha * x3, x3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [48]:
# def generator(z, out_channel_dim, is_train=True, alpha=0.01):
#     """
#     Create the generator network
#     :param z: Input z
#     :param out_channel_dim: The number of channels in the output image
#     :param is_train: Boolean if generator is being used for training
#     :return: The tensor output of the generator
#     """
#     # TODO: Implement Function
#     with tf.variable_scope("generator", reuse=not is_train):
  
#         x1 = tf.layers.dense(z, 7*7*512)
    
#         x1 = tf.reshape(x1, (-1, 7, 7, 512))
#         x1 = tf.layers.batch_normalization(x1, training=is_train)
#         x1 = tf.maximum(alpha * x1, x1)

#         x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
#         x2 = tf.layers.batch_normalization(x2, training=is_train)
#         x2 = tf.maximum(alpha * x2, x2)

#         logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')

#         out = tf.tanh(logits)


#     return out

def generator(z, out_channel_dim, is_train=True, alpha=0.01):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope("generator", reuse=not is_train):
        
        x1 = tf.layers.dense(z, 7*7*256)

        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.nn.dropout(x1, 0.7)
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)

        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
        x2 = tf.nn.dropout(x2, 0.7)
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)

        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
        x3 = tf.nn.dropout(x3, 0.7)
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)

        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')

        out = tf.tanh(logits)


    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [50]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.1
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_logits_real) * (1-smooth))))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [31]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    # Optimize
    
    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')):
        g_train_opt = tf.train.AdamOptimizer(learning_rate = learning_rate, beta1 = beta1).minimize(g_loss, var_list = g_vars)
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [32]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [33]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _, img_width, img_height, img_channels = data_shape

    input_real, input_z, lr = model_inputs(img_width, img_height, img_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, img_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)

    losses = []

    step = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                step += 1
                batch_images = batch_images * 2.0
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, lr:learning_rate})

                if step % 10 == 0:
                    d_loss_training = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    g_loss_training = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(d_loss_training),
                          "Generator Loss: {:.4f}".format(g_loss_training))

                    losses.append((d_loss_training, g_loss_training))

                if step % 100 == 0:
                    show_generator_output(sess, 25, input_z,
                                          out_channel_dim =img_channels, image_mode =data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [51]:
batch_size = 64
z_dim = 128
learning_rate = 0.00017
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.5055... Generator Loss: 2.2510
Epoch 1/2... Discriminator Loss: 0.5758... Generator Loss: 2.0988
Epoch 1/2... Discriminator Loss: 0.4124... Generator Loss: 3.1701
Epoch 1/2... Discriminator Loss: 1.7270... Generator Loss: 0.4071
Epoch 1/2... Discriminator Loss: 0.4689... Generator Loss: 2.5137
Epoch 1/2... Discriminator Loss: 0.5314... Generator Loss: 3.3619
Epoch 1/2... Discriminator Loss: 0.6181... Generator Loss: 4.0992
Epoch 1/2... Discriminator Loss: 0.5122... Generator Loss: 2.3068
Epoch 1/2... Discriminator Loss: 0.7016... Generator Loss: 1.9756
Epoch 1/2... Discriminator Loss: 1.4414... Generator Loss: 0.5979
Epoch 1/2... Discriminator Loss: 0.6948... Generator Loss: 1.7673
Epoch 1/2... Discriminator Loss: 0.7024... Generator Loss: 1.4961
Epoch 1/2... Discriminator Loss: 0.6421... Generator Loss: 1.5886
Epoch 1/2... Discriminator Loss: 0.6139... Generator Loss: 2.9111
Epoch 1/2... Discriminator Loss: 0.5249... Generator Loss: 2.3455
Epoch 1/2... Discriminator Loss: 0.6451... Generator Loss: 2.0966
Epoch 1/2... Discriminator Loss: 0.6860... Generator Loss: 1.6528
Epoch 1/2... Discriminator Loss: 0.7010... Generator Loss: 2.5597
Epoch 1/2... Discriminator Loss: 0.5808... Generator Loss: 2.0851
Epoch 1/2... Discriminator Loss: 0.7907... Generator Loss: 1.3029
Epoch 1/2... Discriminator Loss: 0.5690... Generator Loss: 2.1307
Epoch 1/2... Discriminator Loss: 0.5886... Generator Loss: 1.9219
Epoch 1/2... Discriminator Loss: 0.5440... Generator Loss: 2.6599
Epoch 1/2... Discriminator Loss: 0.6194... Generator Loss: 3.3417
Epoch 1/2... Discriminator Loss: 0.5318... Generator Loss: 2.8321
Epoch 1/2... Discriminator Loss: 0.5098... Generator Loss: 2.6202
Epoch 1/2... Discriminator Loss: 0.5183... Generator Loss: 2.3659
Epoch 1/2... Discriminator Loss: 1.2918... Generator Loss: 0.8278
Epoch 1/2... Discriminator Loss: 0.5742... Generator Loss: 1.9010
Epoch 1/2... Discriminator Loss: 0.5530... Generator Loss: 2.9476
Epoch 1/2... Discriminator Loss: 0.6394... Generator Loss: 1.7415
Epoch 1/2... Discriminator Loss: 0.5329... Generator Loss: 2.2593
Epoch 1/2... Discriminator Loss: 0.4971... Generator Loss: 2.8641
Epoch 1/2... Discriminator Loss: 0.6444... Generator Loss: 1.7695
Epoch 1/2... Discriminator Loss: 0.5057... Generator Loss: 2.5095
Epoch 1/2... Discriminator Loss: 0.5927... Generator Loss: 1.9139
Epoch 1/2... Discriminator Loss: 0.5063... Generator Loss: 2.4573
Epoch 1/2... Discriminator Loss: 0.4942... Generator Loss: 2.4765
Epoch 1/2... Discriminator Loss: 0.6402... Generator Loss: 1.9817
Epoch 1/2... Discriminator Loss: 0.5764... Generator Loss: 2.2000
Epoch 1/2... Discriminator Loss: 0.5364... Generator Loss: 3.0610
Epoch 1/2... Discriminator Loss: 0.9036... Generator Loss: 1.1328
Epoch 1/2... Discriminator Loss: 0.8504... Generator Loss: 3.5189
Epoch 1/2... Discriminator Loss: 0.6748... Generator Loss: 3.3291
Epoch 1/2... Discriminator Loss: 0.7684... Generator Loss: 1.3538
Epoch 1/2... Discriminator Loss: 0.6842... Generator Loss: 1.5923
Epoch 1/2... Discriminator Loss: 0.6296... Generator Loss: 2.9735
Epoch 1/2... Discriminator Loss: 0.4888... Generator Loss: 2.1738
Epoch 1/2... Discriminator Loss: 0.5535... Generator Loss: 2.7686
Epoch 1/2... Discriminator Loss: 0.6628... Generator Loss: 2.9592
Epoch 1/2... Discriminator Loss: 0.6061... Generator Loss: 2.4452
Epoch 1/2... Discriminator Loss: 0.6641... Generator Loss: 3.3218
Epoch 1/2... Discriminator Loss: 0.7340... Generator Loss: 1.2642
Epoch 1/2... Discriminator Loss: 0.6921... Generator Loss: 2.9763
Epoch 1/2... Discriminator Loss: 0.5505... Generator Loss: 2.1248
Epoch 1/2... Discriminator Loss: 0.6095... Generator Loss: 1.7904
Epoch 1/2... Discriminator Loss: 0.5486... Generator Loss: 2.4495
Epoch 1/2... Discriminator Loss: 0.5765... Generator Loss: 2.6676
Epoch 1/2... Discriminator Loss: 0.6229... Generator Loss: 2.4804
Epoch 1/2... Discriminator Loss: 0.6330... Generator Loss: 2.2864
Epoch 1/2... Discriminator Loss: 0.6342... Generator Loss: 2.8559
Epoch 1/2... Discriminator Loss: 0.5918... Generator Loss: 2.0578
Epoch 1/2... Discriminator Loss: 0.5199... Generator Loss: 2.5079
Epoch 1/2... Discriminator Loss: 0.6602... Generator Loss: 1.9436
Epoch 1/2... Discriminator Loss: 0.5778... Generator Loss: 2.2335
Epoch 1/2... Discriminator Loss: 0.6372... Generator Loss: 2.1901
Epoch 1/2... Discriminator Loss: 0.5930... Generator Loss: 2.1703
Epoch 1/2... Discriminator Loss: 0.6238... Generator Loss: 2.5135
Epoch 1/2... Discriminator Loss: 0.5964... Generator Loss: 2.1422
Epoch 1/2... Discriminator Loss: 0.5834... Generator Loss: 1.7946
Epoch 1/2... Discriminator Loss: 0.6048... Generator Loss: 2.0621
Epoch 1/2... Discriminator Loss: 0.5386... Generator Loss: 2.3004
Epoch 1/2... Discriminator Loss: 1.3301... Generator Loss: 0.6366
Epoch 1/2... Discriminator Loss: 1.1720... Generator Loss: 0.8098
Epoch 1/2... Discriminator Loss: 0.6253... Generator Loss: 2.2105
Epoch 1/2... Discriminator Loss: 0.6150... Generator Loss: 1.8978
Epoch 1/2... Discriminator Loss: 0.8073... Generator Loss: 1.2507
Epoch 1/2... Discriminator Loss: 0.7556... Generator Loss: 3.1419
Epoch 1/2... Discriminator Loss: 0.5231... Generator Loss: 2.7403
Epoch 1/2... Discriminator Loss: 0.7703... Generator Loss: 2.9359
Epoch 1/2... Discriminator Loss: 0.5483... Generator Loss: 2.3263
Epoch 1/2... Discriminator Loss: 0.5668... Generator Loss: 2.2975
Epoch 1/2... Discriminator Loss: 0.6519... Generator Loss: 2.1912
Epoch 1/2... Discriminator Loss: 0.5545... Generator Loss: 3.3003
Epoch 1/2... Discriminator Loss: 0.5719... Generator Loss: 2.0965
Epoch 1/2... Discriminator Loss: 0.8021... Generator Loss: 3.3638
Epoch 1/2... Discriminator Loss: 0.7618... Generator Loss: 3.2849
Epoch 1/2... Discriminator Loss: 0.5576... Generator Loss: 2.1610
Epoch 1/2... Discriminator Loss: 0.5882... Generator Loss: 2.5949
Epoch 1/2... Discriminator Loss: 0.6707... Generator Loss: 1.6007
Epoch 1/2... Discriminator Loss: 0.5671... Generator Loss: 2.2156
Epoch 1/2... Discriminator Loss: 0.5293... Generator Loss: 2.5379
Epoch 1/2... Discriminator Loss: 0.6077... Generator Loss: 3.0277
Epoch 2/2... Discriminator Loss: 0.5633... Generator Loss: 2.6671
Epoch 2/2... Discriminator Loss: 0.6123... Generator Loss: 1.7119
Epoch 2/2... Discriminator Loss: 0.6042... Generator Loss: 2.8229
Epoch 2/2... Discriminator Loss: 0.5442... Generator Loss: 2.2844
Epoch 2/2... Discriminator Loss: 0.4981... Generator Loss: 2.2440
Epoch 2/2... Discriminator Loss: 0.6657... Generator Loss: 1.5121
Epoch 2/2... Discriminator Loss: 0.7515... Generator Loss: 1.3735
Epoch 2/2... Discriminator Loss: 0.5179... Generator Loss: 2.4297
Epoch 2/2... Discriminator Loss: 0.5961... Generator Loss: 2.5138
Epoch 2/2... Discriminator Loss: 0.6326... Generator Loss: 2.0872
Epoch 2/2... Discriminator Loss: 0.5282... Generator Loss: 1.9951
Epoch 2/2... Discriminator Loss: 0.6338... Generator Loss: 2.1002
Epoch 2/2... Discriminator Loss: 0.8279... Generator Loss: 1.4425
Epoch 2/2... Discriminator Loss: 0.6106... Generator Loss: 2.8721
Epoch 2/2... Discriminator Loss: 0.5353... Generator Loss: 2.5966
Epoch 2/2... Discriminator Loss: 1.0872... Generator Loss: 0.9325
Epoch 2/2... Discriminator Loss: 0.5479... Generator Loss: 1.7859
Epoch 2/2... Discriminator Loss: 0.5242... Generator Loss: 2.6000
Epoch 2/2... Discriminator Loss: 0.5542... Generator Loss: 2.4024
Epoch 2/2... Discriminator Loss: 0.6994... Generator Loss: 1.3927
Epoch 2/2... Discriminator Loss: 0.5184... Generator Loss: 2.6906
Epoch 2/2... Discriminator Loss: 0.5207... Generator Loss: 2.2767
Epoch 2/2... Discriminator Loss: 0.5363... Generator Loss: 2.4314
Epoch 2/2... Discriminator Loss: 0.5654... Generator Loss: 1.9416
Epoch 2/2... Discriminator Loss: 0.6803... Generator Loss: 3.1367
Epoch 2/2... Discriminator Loss: 0.7206... Generator Loss: 1.7037
Epoch 2/2... Discriminator Loss: 0.5791... Generator Loss: 2.1993
Epoch 2/2... Discriminator Loss: 1.0305... Generator Loss: 1.1817
Epoch 2/2... Discriminator Loss: 0.5201... Generator Loss: 2.5935
Epoch 2/2... Discriminator Loss: 0.6198... Generator Loss: 2.0244
Epoch 2/2... Discriminator Loss: 0.4980... Generator Loss: 2.7802
Epoch 2/2... Discriminator Loss: 0.7412... Generator Loss: 4.0636
Epoch 2/2... Discriminator Loss: 0.4909... Generator Loss: 2.9347
Epoch 2/2... Discriminator Loss: 0.6631... Generator Loss: 1.7099
Epoch 2/2... Discriminator Loss: 0.4999... Generator Loss: 2.0914
Epoch 2/2... Discriminator Loss: 0.4746... Generator Loss: 3.1592
Epoch 2/2... Discriminator Loss: 0.4568... Generator Loss: 2.8560
Epoch 2/2... Discriminator Loss: 0.4284... Generator Loss: 3.6517
Epoch 2/2... Discriminator Loss: 0.5204... Generator Loss: 2.0106
Epoch 2/2... Discriminator Loss: 0.5540... Generator Loss: 2.0898
Epoch 2/2... Discriminator Loss: 0.5381... Generator Loss: 2.2208
Epoch 2/2... Discriminator Loss: 0.4967... Generator Loss: 2.6596
Epoch 2/2... Discriminator Loss: 0.8062... Generator Loss: 1.4043
Epoch 2/2... Discriminator Loss: 0.4754... Generator Loss: 3.0509
Epoch 2/2... Discriminator Loss: 0.4662... Generator Loss: 2.8451
Epoch 2/2... Discriminator Loss: 0.5294... Generator Loss: 2.1979
Epoch 2/2... Discriminator Loss: 0.5157... Generator Loss: 2.3749
Epoch 2/2... Discriminator Loss: 0.5110... Generator Loss: 3.4760
Epoch 2/2... Discriminator Loss: 0.5046... Generator Loss: 1.9144
Epoch 2/2... Discriminator Loss: 0.6656... Generator Loss: 1.6196
Epoch 2/2... Discriminator Loss: 0.4978... Generator Loss: 2.8148
Epoch 2/2... Discriminator Loss: 0.4634... Generator Loss: 2.8196
Epoch 2/2... Discriminator Loss: 2.0480... Generator Loss: 0.4525
Epoch 2/2... Discriminator Loss: 0.6413... Generator Loss: 1.5518
Epoch 2/2... Discriminator Loss: 0.5430... Generator Loss: 2.0210
Epoch 2/2... Discriminator Loss: 0.5228... Generator Loss: 1.9449
Epoch 2/2... Discriminator Loss: 0.6586... Generator Loss: 1.4308
Epoch 2/2... Discriminator Loss: 0.5261... Generator Loss: 1.8538
Epoch 2/2... Discriminator Loss: 0.6031... Generator Loss: 1.8688
Epoch 2/2... Discriminator Loss: 0.6458... Generator Loss: 1.7598
Epoch 2/2... Discriminator Loss: 0.4559... Generator Loss: 2.7469
Epoch 2/2... Discriminator Loss: 0.4589... Generator Loss: 2.9856
Epoch 2/2... Discriminator Loss: 0.5081... Generator Loss: 2.5994
Epoch 2/2... Discriminator Loss: 0.5190... Generator Loss: 2.4786
Epoch 2/2... Discriminator Loss: 0.6097... Generator Loss: 1.9153
Epoch 2/2... Discriminator Loss: 0.4184... Generator Loss: 2.8348
Epoch 2/2... Discriminator Loss: 0.5530... Generator Loss: 2.5535
Epoch 2/2... Discriminator Loss: 0.4874... Generator Loss: 2.6329
Epoch 2/2... Discriminator Loss: 0.5735... Generator Loss: 2.0497
Epoch 2/2... Discriminator Loss: 0.4956... Generator Loss: 2.2391
Epoch 2/2... Discriminator Loss: 0.6111... Generator Loss: 3.3651
Epoch 2/2... Discriminator Loss: 0.6054... Generator Loss: 1.6780
Epoch 2/2... Discriminator Loss: 0.5136... Generator Loss: 2.5853
Epoch 2/2... Discriminator Loss: 1.3071... Generator Loss: 4.1230
Epoch 2/2... Discriminator Loss: 0.4574... Generator Loss: 2.4488
Epoch 2/2... Discriminator Loss: 0.4921... Generator Loss: 2.7993
Epoch 2/2... Discriminator Loss: 0.4642... Generator Loss: 3.0116
Epoch 2/2... Discriminator Loss: 0.5789... Generator Loss: 1.8570
Epoch 2/2... Discriminator Loss: 0.4916... Generator Loss: 2.4321
Epoch 2/2... Discriminator Loss: 0.4971... Generator Loss: 2.5041
Epoch 2/2... Discriminator Loss: 0.6250... Generator Loss: 1.8798
Epoch 2/2... Discriminator Loss: 0.5969... Generator Loss: 3.9021
Epoch 2/2... Discriminator Loss: 0.4864... Generator Loss: 3.0722
Epoch 2/2... Discriminator Loss: 0.7002... Generator Loss: 1.6758
Epoch 2/2... Discriminator Loss: 0.4845... Generator Loss: 2.8087
Epoch 2/2... Discriminator Loss: 0.5515... Generator Loss: 1.9495
Epoch 2/2... Discriminator Loss: 0.4903... Generator Loss: 2.5144
Epoch 2/2... Discriminator Loss: 0.4997... Generator Loss: 2.9994
Epoch 2/2... Discriminator Loss: 0.5357... Generator Loss: 3.2677
Epoch 2/2... Discriminator Loss: 0.5358... Generator Loss: 2.5512
Epoch 2/2... Discriminator Loss: 0.4413... Generator Loss: 2.9493
Epoch 2/2... Discriminator Loss: 0.5342... Generator Loss: 3.3072
Epoch 2/2... Discriminator Loss: 0.4371... Generator Loss: 3.0452
Epoch 2/2... Discriminator Loss: 0.4941... Generator Loss: 2.7234

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [52]:
batch_size = 32
z_dim = 128
learning_rate = 0.00017
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.4258... Generator Loss: 3.2936
Epoch 1/1... Discriminator Loss: 0.3764... Generator Loss: 4.7981
Epoch 1/1... Discriminator Loss: 0.3612... Generator Loss: 3.6686
Epoch 1/1... Discriminator Loss: 0.3846... Generator Loss: 5.4502
Epoch 1/1... Discriminator Loss: 0.4030... Generator Loss: 3.8029
Epoch 1/1... Discriminator Loss: 0.3561... Generator Loss: 4.8293
Epoch 1/1... Discriminator Loss: 0.3922... Generator Loss: 3.7065
Epoch 1/1... Discriminator Loss: 0.4362... Generator Loss: 3.1345
Epoch 1/1... Discriminator Loss: 0.8279... Generator Loss: 1.5211
Epoch 1/1... Discriminator Loss: 0.8718... Generator Loss: 1.2217
Epoch 1/1... Discriminator Loss: 0.5780... Generator Loss: 4.8825
Epoch 1/1... Discriminator Loss: 0.4214... Generator Loss: 2.9511
Epoch 1/1... Discriminator Loss: 0.3937... Generator Loss: 4.0681
Epoch 1/1... Discriminator Loss: 0.4550... Generator Loss: 2.9110
Epoch 1/1... Discriminator Loss: 0.3935... Generator Loss: 4.5063
Epoch 1/1... Discriminator Loss: 0.4282... Generator Loss: 4.8618
Epoch 1/1... Discriminator Loss: 0.3770... Generator Loss: 4.4966
Epoch 1/1... Discriminator Loss: 0.3830... Generator Loss: 4.1007
Epoch 1/1... Discriminator Loss: 0.3745... Generator Loss: 5.3027
Epoch 1/1... Discriminator Loss: 0.4143... Generator Loss: 2.9711
Epoch 1/1... Discriminator Loss: 0.5186... Generator Loss: 5.7020
Epoch 1/1... Discriminator Loss: 0.3988... Generator Loss: 3.3797
Epoch 1/1... Discriminator Loss: 0.3630... Generator Loss: 5.5098
Epoch 1/1... Discriminator Loss: 0.3993... Generator Loss: 3.1425
Epoch 1/1... Discriminator Loss: 0.3768... Generator Loss: 3.9643
Epoch 1/1... Discriminator Loss: 0.3653... Generator Loss: 4.2015
Epoch 1/1... Discriminator Loss: 0.3461... Generator Loss: 5.0194
Epoch 1/1... Discriminator Loss: 0.3618... Generator Loss: 4.1595
Epoch 1/1... Discriminator Loss: 0.4266... Generator Loss: 2.8299
Epoch 1/1... Discriminator Loss: 0.3990... Generator Loss: 3.4729
Epoch 1/1... Discriminator Loss: 0.3554... Generator Loss: 4.8521
Epoch 1/1... Discriminator Loss: 0.3638... Generator Loss: 4.3865
Epoch 1/1... Discriminator Loss: 0.7187... Generator Loss: 8.3213
Epoch 1/1... Discriminator Loss: 1.1453... Generator Loss: 1.1415
Epoch 1/1... Discriminator Loss: 0.6687... Generator Loss: 1.6021
Epoch 1/1... Discriminator Loss: 0.3882... Generator Loss: 4.4752
Epoch 1/1... Discriminator Loss: 0.4175... Generator Loss: 3.2710
Epoch 1/1... Discriminator Loss: 0.3933... Generator Loss: 3.5322
Epoch 1/1... Discriminator Loss: 0.3860... Generator Loss: 3.6559
Epoch 1/1... Discriminator Loss: 0.3893... Generator Loss: 3.5581
Epoch 1/1... Discriminator Loss: 0.3892... Generator Loss: 3.2843
Epoch 1/1... Discriminator Loss: 0.4053... Generator Loss: 2.7529
Epoch 1/1... Discriminator Loss: 0.4767... Generator Loss: 2.1579
Epoch 1/1... Discriminator Loss: 0.7763... Generator Loss: 1.7240
Epoch 1/1... Discriminator Loss: 0.4745... Generator Loss: 2.8909
Epoch 1/1... Discriminator Loss: 0.3846... Generator Loss: 4.5767
Epoch 1/1... Discriminator Loss: 0.4877... Generator Loss: 2.5893
Epoch 1/1... Discriminator Loss: 0.3971... Generator Loss: 3.7787
Epoch 1/1... Discriminator Loss: 0.4986... Generator Loss: 2.2710
Epoch 1/1... Discriminator Loss: 0.5775... Generator Loss: 2.0336
Epoch 1/1... Discriminator Loss: 0.4133... Generator Loss: 3.5470
Epoch 1/1... Discriminator Loss: 0.4329... Generator Loss: 3.2136
Epoch 1/1... Discriminator Loss: 0.9031... Generator Loss: 0.9860
Epoch 1/1... Discriminator Loss: 0.4151... Generator Loss: 3.1533
Epoch 1/1... Discriminator Loss: 0.4865... Generator Loss: 3.4649
Epoch 1/1... Discriminator Loss: 0.4414... Generator Loss: 3.3915
Epoch 1/1... Discriminator Loss: 0.5282... Generator Loss: 2.1546
Epoch 1/1... Discriminator Loss: 0.4423... Generator Loss: 2.8756
Epoch 1/1... Discriminator Loss: 0.4794... Generator Loss: 3.6581
Epoch 1/1... Discriminator Loss: 0.5082... Generator Loss: 2.7946
Epoch 1/1... Discriminator Loss: 0.4818... Generator Loss: 2.7621
Epoch 1/1... Discriminator Loss: 0.4020... Generator Loss: 4.0324
Epoch 1/1... Discriminator Loss: 0.4079... Generator Loss: 3.9832
Epoch 1/1... Discriminator Loss: 0.8273... Generator Loss: 1.3998
Epoch 1/1... Discriminator Loss: 0.4863... Generator Loss: 2.5996
Epoch 1/1... Discriminator Loss: 0.4323... Generator Loss: 3.6685
Epoch 1/1... Discriminator Loss: 0.4542... Generator Loss: 2.4666
Epoch 1/1... Discriminator Loss: 0.5107... Generator Loss: 2.5562
Epoch 1/1... Discriminator Loss: 0.4062... Generator Loss: 3.6357
Epoch 1/1... Discriminator Loss: 0.4993... Generator Loss: 2.5257
Epoch 1/1... Discriminator Loss: 0.5415... Generator Loss: 2.0273
Epoch 1/1... Discriminator Loss: 0.4466... Generator Loss: 3.8532
Epoch 1/1... Discriminator Loss: 0.3985... Generator Loss: 3.3741
Epoch 1/1... Discriminator Loss: 0.3877... Generator Loss: 3.6289
Epoch 1/1... Discriminator Loss: 0.4312... Generator Loss: 3.2074
Epoch 1/1... Discriminator Loss: 0.4164... Generator Loss: 3.1192
Epoch 1/1... Discriminator Loss: 0.5413... Generator Loss: 5.2289
Epoch 1/1... Discriminator Loss: 0.4390... Generator Loss: 3.1152
Epoch 1/1... Discriminator Loss: 0.4352... Generator Loss: 4.3341
Epoch 1/1... Discriminator Loss: 0.5519... Generator Loss: 2.0523
Epoch 1/1... Discriminator Loss: 0.4425... Generator Loss: 2.9623
Epoch 1/1... Discriminator Loss: 0.8615... Generator Loss: 1.3122
Epoch 1/1... Discriminator Loss: 0.6293... Generator Loss: 1.9513
Epoch 1/1... Discriminator Loss: 0.6525... Generator Loss: 1.9398
Epoch 1/1... Discriminator Loss: 0.9897... Generator Loss: 1.2510
Epoch 1/1... Discriminator Loss: 0.4641... Generator Loss: 2.6151
Epoch 1/1... Discriminator Loss: 0.5936... Generator Loss: 1.9022
Epoch 1/1... Discriminator Loss: 1.2874... Generator Loss: 0.8902
Epoch 1/1... Discriminator Loss: 0.4282... Generator Loss: 3.1106
Epoch 1/1... Discriminator Loss: 0.5239... Generator Loss: 2.2461
Epoch 1/1... Discriminator Loss: 0.5677... Generator Loss: 2.3354
Epoch 1/1... Discriminator Loss: 0.7193... Generator Loss: 5.0701
Epoch 1/1... Discriminator Loss: 0.5448... Generator Loss: 2.3978
Epoch 1/1... Discriminator Loss: 0.6755... Generator Loss: 3.9914
Epoch 1/1... Discriminator Loss: 1.0822... Generator Loss: 0.8556
Epoch 1/1... Discriminator Loss: 0.5600... Generator Loss: 3.6834
Epoch 1/1... Discriminator Loss: 0.4654... Generator Loss: 2.7965
Epoch 1/1... Discriminator Loss: 0.5493... Generator Loss: 2.0563
Epoch 1/1... Discriminator Loss: 0.4870... Generator Loss: 2.6258
Epoch 1/1... Discriminator Loss: 0.5244... Generator Loss: 2.5742
Epoch 1/1... Discriminator Loss: 0.4996... Generator Loss: 2.3853
Epoch 1/1... Discriminator Loss: 0.5225... Generator Loss: 2.9171
Epoch 1/1... Discriminator Loss: 0.7823... Generator Loss: 1.4246
Epoch 1/1... Discriminator Loss: 0.7364... Generator Loss: 1.4587
Epoch 1/1... Discriminator Loss: 0.6145... Generator Loss: 2.8294
Epoch 1/1... Discriminator Loss: 0.6996... Generator Loss: 1.6659
Epoch 1/1... Discriminator Loss: 0.7531... Generator Loss: 4.1785
Epoch 1/1... Discriminator Loss: 0.5054... Generator Loss: 2.6564
Epoch 1/1... Discriminator Loss: 1.5606... Generator Loss: 0.6437
Epoch 1/1... Discriminator Loss: 0.6305... Generator Loss: 1.9018
Epoch 1/1... Discriminator Loss: 0.5963... Generator Loss: 2.8163
Epoch 1/1... Discriminator Loss: 0.4888... Generator Loss: 3.5164
Epoch 1/1... Discriminator Loss: 0.5150... Generator Loss: 2.6197
Epoch 1/1... Discriminator Loss: 0.8797... Generator Loss: 1.0180
Epoch 1/1... Discriminator Loss: 0.6950... Generator Loss: 1.9329
Epoch 1/1... Discriminator Loss: 0.5878... Generator Loss: 1.8102
Epoch 1/1... Discriminator Loss: 0.5194... Generator Loss: 1.8183
Epoch 1/1... Discriminator Loss: 0.6829... Generator Loss: 2.9546
Epoch 1/1... Discriminator Loss: 0.8565... Generator Loss: 1.2447
Epoch 1/1... Discriminator Loss: 0.4939... Generator Loss: 2.6581
Epoch 1/1... Discriminator Loss: 0.5783... Generator Loss: 1.8629
Epoch 1/1... Discriminator Loss: 0.5915... Generator Loss: 1.9233
Epoch 1/1... Discriminator Loss: 0.5358... Generator Loss: 2.1582
Epoch 1/1... Discriminator Loss: 0.6482... Generator Loss: 3.1562
Epoch 1/1... Discriminator Loss: 0.8801... Generator Loss: 1.0772
Epoch 1/1... Discriminator Loss: 0.5365... Generator Loss: 2.3185
Epoch 1/1... Discriminator Loss: 0.5527... Generator Loss: 1.8262
Epoch 1/1... Discriminator Loss: 0.6914... Generator Loss: 1.4098
Epoch 1/1... Discriminator Loss: 0.5485... Generator Loss: 2.5174
Epoch 1/1... Discriminator Loss: 0.6656... Generator Loss: 3.0792
Epoch 1/1... Discriminator Loss: 0.5542... Generator Loss: 1.9449
Epoch 1/1... Discriminator Loss: 0.5707... Generator Loss: 2.8386
Epoch 1/1... Discriminator Loss: 0.7584... Generator Loss: 1.4480
Epoch 1/1... Discriminator Loss: 0.4795... Generator Loss: 2.1816
Epoch 1/1... Discriminator Loss: 0.6958... Generator Loss: 1.7298
Epoch 1/1... Discriminator Loss: 0.8145... Generator Loss: 1.4580
Epoch 1/1... Discriminator Loss: 0.5719... Generator Loss: 2.1099
Epoch 1/1... Discriminator Loss: 0.7271... Generator Loss: 1.5870
Epoch 1/1... Discriminator Loss: 0.8961... Generator Loss: 1.3261
Epoch 1/1... Discriminator Loss: 0.5199... Generator Loss: 2.1099
Epoch 1/1... Discriminator Loss: 0.7515... Generator Loss: 2.8874
Epoch 1/1... Discriminator Loss: 0.5621... Generator Loss: 2.4329
Epoch 1/1... Discriminator Loss: 0.5873... Generator Loss: 2.4453
Epoch 1/1... Discriminator Loss: 0.8175... Generator Loss: 1.3543
Epoch 1/1... Discriminator Loss: 0.6103... Generator Loss: 1.6512
Epoch 1/1... Discriminator Loss: 1.1488... Generator Loss: 0.9958
Epoch 1/1... Discriminator Loss: 0.5525... Generator Loss: 1.9131
Epoch 1/1... Discriminator Loss: 0.5541... Generator Loss: 2.2569
Epoch 1/1... Discriminator Loss: 0.7214... Generator Loss: 1.6070
Epoch 1/1... Discriminator Loss: 0.5023... Generator Loss: 2.8916
Epoch 1/1... Discriminator Loss: 0.5927... Generator Loss: 2.3299
Epoch 1/1... Discriminator Loss: 0.9410... Generator Loss: 0.9899
Epoch 1/1... Discriminator Loss: 0.6313... Generator Loss: 2.2453
Epoch 1/1... Discriminator Loss: 0.5658... Generator Loss: 1.7140
Epoch 1/1... Discriminator Loss: 0.6596... Generator Loss: 2.0975
Epoch 1/1... Discriminator Loss: 0.5555... Generator Loss: 2.3817
Epoch 1/1... Discriminator Loss: 0.5679... Generator Loss: 2.0641
Epoch 1/1... Discriminator Loss: 0.5914... Generator Loss: 2.0255
Epoch 1/1... Discriminator Loss: 0.9263... Generator Loss: 1.2234
Epoch 1/1... Discriminator Loss: 0.6608... Generator Loss: 1.6584
Epoch 1/1... Discriminator Loss: 0.6904... Generator Loss: 2.3180
Epoch 1/1... Discriminator Loss: 0.6355... Generator Loss: 2.3852
Epoch 1/1... Discriminator Loss: 0.6630... Generator Loss: 1.7020
Epoch 1/1... Discriminator Loss: 0.5637... Generator Loss: 2.4317
Epoch 1/1... Discriminator Loss: 0.6699... Generator Loss: 1.4811
Epoch 1/1... Discriminator Loss: 0.5982... Generator Loss: 2.2361
Epoch 1/1... Discriminator Loss: 0.6512... Generator Loss: 1.9623
Epoch 1/1... Discriminator Loss: 0.4896... Generator Loss: 2.5032
Epoch 1/1... Discriminator Loss: 0.6049... Generator Loss: 1.4879
Epoch 1/1... Discriminator Loss: 0.7881... Generator Loss: 1.4466
Epoch 1/1... Discriminator Loss: 0.6020... Generator Loss: 2.3127
Epoch 1/1... Discriminator Loss: 0.5928... Generator Loss: 2.1772
Epoch 1/1... Discriminator Loss: 0.6799... Generator Loss: 1.4135
Epoch 1/1... Discriminator Loss: 0.6369... Generator Loss: 1.7048
Epoch 1/1... Discriminator Loss: 0.6094... Generator Loss: 1.8307
Epoch 1/1... Discriminator Loss: 0.6720... Generator Loss: 1.6266
Epoch 1/1... Discriminator Loss: 0.5482... Generator Loss: 2.3264
Epoch 1/1... Discriminator Loss: 0.5615... Generator Loss: 2.1481
Epoch 1/1... Discriminator Loss: 0.6458... Generator Loss: 1.5649
Epoch 1/1... Discriminator Loss: 0.5310... Generator Loss: 2.1923
Epoch 1/1... Discriminator Loss: 0.6316... Generator Loss: 1.7942
Epoch 1/1... Discriminator Loss: 0.6588... Generator Loss: 1.6322
Epoch 1/1... Discriminator Loss: 0.5831... Generator Loss: 1.6970
Epoch 1/1... Discriminator Loss: 0.6887... Generator Loss: 1.6634
Epoch 1/1... Discriminator Loss: 0.5171... Generator Loss: 2.4055
Epoch 1/1... Discriminator Loss: 0.5878... Generator Loss: 2.5619
Epoch 1/1... Discriminator Loss: 0.5701... Generator Loss: 2.9495
Epoch 1/1... Discriminator Loss: 0.5763... Generator Loss: 2.4778
Epoch 1/1... Discriminator Loss: 0.6681... Generator Loss: 1.7988
Epoch 1/1... Discriminator Loss: 0.7274... Generator Loss: 1.5127
Epoch 1/1... Discriminator Loss: 0.5634... Generator Loss: 3.1940
Epoch 1/1... Discriminator Loss: 0.6390... Generator Loss: 3.0644
Epoch 1/1... Discriminator Loss: 0.5490... Generator Loss: 2.1555
Epoch 1/1... Discriminator Loss: 0.6140... Generator Loss: 3.2154
Epoch 1/1... Discriminator Loss: 0.6604... Generator Loss: 3.2337
Epoch 1/1... Discriminator Loss: 0.5149... Generator Loss: 2.3712
Epoch 1/1... Discriminator Loss: 1.3232... Generator Loss: 0.5643
Epoch 1/1... Discriminator Loss: 0.7327... Generator Loss: 1.5972
Epoch 1/1... Discriminator Loss: 0.5991... Generator Loss: 2.1112
Epoch 1/1... Discriminator Loss: 0.8941... Generator Loss: 0.9932
Epoch 1/1... Discriminator Loss: 0.5365... Generator Loss: 2.0791
Epoch 1/1... Discriminator Loss: 0.7211... Generator Loss: 2.7726
Epoch 1/1... Discriminator Loss: 0.8126... Generator Loss: 1.1886
Epoch 1/1... Discriminator Loss: 0.5255... Generator Loss: 2.2088
Epoch 1/1... Discriminator Loss: 0.5645... Generator Loss: 2.2681
Epoch 1/1... Discriminator Loss: 0.6869... Generator Loss: 1.5454
Epoch 1/1... Discriminator Loss: 0.5904... Generator Loss: 1.6426
Epoch 1/1... Discriminator Loss: 0.6592... Generator Loss: 1.6806
Epoch 1/1... Discriminator Loss: 0.6973... Generator Loss: 1.3487
Epoch 1/1... Discriminator Loss: 0.8229... Generator Loss: 1.2906
Epoch 1/1... Discriminator Loss: 0.5699... Generator Loss: 2.2863
Epoch 1/1... Discriminator Loss: 0.5916... Generator Loss: 1.9465
Epoch 1/1... Discriminator Loss: 0.5759... Generator Loss: 2.9011
Epoch 1/1... Discriminator Loss: 0.5603... Generator Loss: 1.6467
Epoch 1/1... Discriminator Loss: 0.5793... Generator Loss: 1.9768
Epoch 1/1... Discriminator Loss: 0.5792... Generator Loss: 2.8683
Epoch 1/1... Discriminator Loss: 0.7842... Generator Loss: 2.4419
Epoch 1/1... Discriminator Loss: 0.5257... Generator Loss: 2.4035
Epoch 1/1... Discriminator Loss: 0.9245... Generator Loss: 1.1816
Epoch 1/1... Discriminator Loss: 0.7091... Generator Loss: 1.8355
Epoch 1/1... Discriminator Loss: 0.5826... Generator Loss: 2.3208
Epoch 1/1... Discriminator Loss: 0.6834... Generator Loss: 1.6064
Epoch 1/1... Discriminator Loss: 0.6376... Generator Loss: 1.4726
Epoch 1/1... Discriminator Loss: 0.6394... Generator Loss: 2.1159
Epoch 1/1... Discriminator Loss: 0.7819... Generator Loss: 1.5793
Epoch 1/1... Discriminator Loss: 0.5433... Generator Loss: 1.6028
Epoch 1/1... Discriminator Loss: 0.5852... Generator Loss: 1.8120
Epoch 1/1... Discriminator Loss: 0.5160... Generator Loss: 2.1677
Epoch 1/1... Discriminator Loss: 0.6061... Generator Loss: 2.8844
Epoch 1/1... Discriminator Loss: 0.6573... Generator Loss: 1.5108
Epoch 1/1... Discriminator Loss: 0.5258... Generator Loss: 2.2843
Epoch 1/1... Discriminator Loss: 0.6017... Generator Loss: 1.9444
Epoch 1/1... Discriminator Loss: 0.7032... Generator Loss: 1.5727
Epoch 1/1... Discriminator Loss: 0.5152... Generator Loss: 2.0977
Epoch 1/1... Discriminator Loss: 0.6549... Generator Loss: 2.9677
Epoch 1/1... Discriminator Loss: 0.7688... Generator Loss: 1.5300
Epoch 1/1... Discriminator Loss: 0.8594... Generator Loss: 3.0025
Epoch 1/1... Discriminator Loss: 0.6939... Generator Loss: 1.9751
Epoch 1/1... Discriminator Loss: 0.8055... Generator Loss: 1.4890
Epoch 1/1... Discriminator Loss: 0.5770... Generator Loss: 1.9337
Epoch 1/1... Discriminator Loss: 0.7103... Generator Loss: 1.2642
Epoch 1/1... Discriminator Loss: 0.7164... Generator Loss: 2.4688
Epoch 1/1... Discriminator Loss: 0.5994... Generator Loss: 1.7227
Epoch 1/1... Discriminator Loss: 0.4920... Generator Loss: 2.2789
Epoch 1/1... Discriminator Loss: 0.5541... Generator Loss: 2.4956
Epoch 1/1... Discriminator Loss: 0.6095... Generator Loss: 2.3593
Epoch 1/1... Discriminator Loss: 0.6779... Generator Loss: 1.0783
Epoch 1/1... Discriminator Loss: 0.6786... Generator Loss: 1.3638
Epoch 1/1... Discriminator Loss: 0.6474... Generator Loss: 2.4387
Epoch 1/1... Discriminator Loss: 0.6353... Generator Loss: 2.9681
Epoch 1/1... Discriminator Loss: 0.7234... Generator Loss: 1.8756
Epoch 1/1... Discriminator Loss: 0.6043... Generator Loss: 1.9750
Epoch 1/1... Discriminator Loss: 0.6098... Generator Loss: 2.0553
Epoch 1/1... Discriminator Loss: 0.5773... Generator Loss: 2.1234
Epoch 1/1... Discriminator Loss: 0.5112... Generator Loss: 2.2611
Epoch 1/1... Discriminator Loss: 0.6931... Generator Loss: 1.6559
Epoch 1/1... Discriminator Loss: 0.5947... Generator Loss: 2.2244
Epoch 1/1... Discriminator Loss: 0.6459... Generator Loss: 1.7165
Epoch 1/1... Discriminator Loss: 0.8015... Generator Loss: 1.4032
Epoch 1/1... Discriminator Loss: 0.6647... Generator Loss: 1.7078
Epoch 1/1... Discriminator Loss: 0.5121... Generator Loss: 2.1911
Epoch 1/1... Discriminator Loss: 0.6311... Generator Loss: 1.6430
Epoch 1/1... Discriminator Loss: 0.9146... Generator Loss: 1.1285
Epoch 1/1... Discriminator Loss: 0.5901... Generator Loss: 2.0500
Epoch 1/1... Discriminator Loss: 0.6143... Generator Loss: 3.0616
Epoch 1/1... Discriminator Loss: 0.6014... Generator Loss: 1.9802
Epoch 1/1... Discriminator Loss: 0.7455... Generator Loss: 1.3749
Epoch 1/1... Discriminator Loss: 0.5470... Generator Loss: 2.2361
Epoch 1/1... Discriminator Loss: 0.7218... Generator Loss: 2.0263
Epoch 1/1... Discriminator Loss: 0.6677... Generator Loss: 1.5788
Epoch 1/1... Discriminator Loss: 0.9976... Generator Loss: 1.0239
Epoch 1/1... Discriminator Loss: 1.0360... Generator Loss: 1.0173
Epoch 1/1... Discriminator Loss: 0.5495... Generator Loss: 2.4596
Epoch 1/1... Discriminator Loss: 0.5795... Generator Loss: 1.7556
Epoch 1/1... Discriminator Loss: 0.6183... Generator Loss: 1.8836
Epoch 1/1... Discriminator Loss: 0.5025... Generator Loss: 2.8545
Epoch 1/1... Discriminator Loss: 0.7170... Generator Loss: 1.9888
Epoch 1/1... Discriminator Loss: 0.5617... Generator Loss: 2.3756
Epoch 1/1... Discriminator Loss: 0.6892... Generator Loss: 3.0051
Epoch 1/1... Discriminator Loss: 0.5286... Generator Loss: 2.1119
Epoch 1/1... Discriminator Loss: 0.7120... Generator Loss: 1.3851
Epoch 1/1... Discriminator Loss: 0.7144... Generator Loss: 1.6780
Epoch 1/1... Discriminator Loss: 0.6736... Generator Loss: 1.5713
Epoch 1/1... Discriminator Loss: 0.6774... Generator Loss: 1.5474
Epoch 1/1... Discriminator Loss: 0.4972... Generator Loss: 2.4682
Epoch 1/1... Discriminator Loss: 0.6298... Generator Loss: 2.5073
Epoch 1/1... Discriminator Loss: 0.6302... Generator Loss: 1.7769
Epoch 1/1... Discriminator Loss: 0.5305... Generator Loss: 2.3697
Epoch 1/1... Discriminator Loss: 0.7789... Generator Loss: 1.3068
Epoch 1/1... Discriminator Loss: 0.9294... Generator Loss: 1.9731
Epoch 1/1... Discriminator Loss: 0.9029... Generator Loss: 1.2126
Epoch 1/1... Discriminator Loss: 0.5952... Generator Loss: 1.6590
Epoch 1/1... Discriminator Loss: 0.4816... Generator Loss: 3.2890
Epoch 1/1... Discriminator Loss: 0.9734... Generator Loss: 2.2703
Epoch 1/1... Discriminator Loss: 0.5180... Generator Loss: 2.2447
Epoch 1/1... Discriminator Loss: 0.5359... Generator Loss: 2.4793
Epoch 1/1... Discriminator Loss: 0.5010... Generator Loss: 2.6306
Epoch 1/1... Discriminator Loss: 0.5823... Generator Loss: 2.6087
Epoch 1/1... Discriminator Loss: 0.5141... Generator Loss: 2.3737
Epoch 1/1... Discriminator Loss: 0.5129... Generator Loss: 2.1775
Epoch 1/1... Discriminator Loss: 0.5472... Generator Loss: 2.5737
Epoch 1/1... Discriminator Loss: 0.5250... Generator Loss: 2.9036
Epoch 1/1... Discriminator Loss: 0.5054... Generator Loss: 2.3659
Epoch 1/1... Discriminator Loss: 0.9215... Generator Loss: 1.1174
Epoch 1/1... Discriminator Loss: 0.7329... Generator Loss: 1.3570
Epoch 1/1... Discriminator Loss: 0.9273... Generator Loss: 1.1008
Epoch 1/1... Discriminator Loss: 0.8134... Generator Loss: 3.1867
Epoch 1/1... Discriminator Loss: 0.7203... Generator Loss: 1.5903
Epoch 1/1... Discriminator Loss: 0.6670... Generator Loss: 1.4026
Epoch 1/1... Discriminator Loss: 0.6875... Generator Loss: 1.5746
Epoch 1/1... Discriminator Loss: 0.5527... Generator Loss: 2.4809
Epoch 1/1... Discriminator Loss: 0.5336... Generator Loss: 2.9315
Epoch 1/1... Discriminator Loss: 0.5802... Generator Loss: 1.7605
Epoch 1/1... Discriminator Loss: 0.6349... Generator Loss: 1.9182
Epoch 1/1... Discriminator Loss: 0.5723... Generator Loss: 2.1715
Epoch 1/1... Discriminator Loss: 0.5421... Generator Loss: 1.9197
Epoch 1/1... Discriminator Loss: 0.7837... Generator Loss: 1.4361
Epoch 1/1... Discriminator Loss: 0.6397... Generator Loss: 2.7214
Epoch 1/1... Discriminator Loss: 0.6195... Generator Loss: 1.5756
Epoch 1/1... Discriminator Loss: 0.5426... Generator Loss: 1.9266
Epoch 1/1... Discriminator Loss: 0.5589... Generator Loss: 1.9504
Epoch 1/1... Discriminator Loss: 0.5293... Generator Loss: 1.9500
Epoch 1/1... Discriminator Loss: 0.5535... Generator Loss: 2.6466
Epoch 1/1... Discriminator Loss: 0.5612... Generator Loss: 2.5638
Epoch 1/1... Discriminator Loss: 0.6083... Generator Loss: 3.0837
Epoch 1/1... Discriminator Loss: 0.6244... Generator Loss: 1.7735
Epoch 1/1... Discriminator Loss: 0.4878... Generator Loss: 2.4941
Epoch 1/1... Discriminator Loss: 1.0399... Generator Loss: 0.7940
Epoch 1/1... Discriminator Loss: 1.0511... Generator Loss: 0.8369
Epoch 1/1... Discriminator Loss: 0.7256... Generator Loss: 1.4607
Epoch 1/1... Discriminator Loss: 0.4898... Generator Loss: 2.5358
Epoch 1/1... Discriminator Loss: 0.7964... Generator Loss: 1.4679
Epoch 1/1... Discriminator Loss: 0.6813... Generator Loss: 1.5253
Epoch 1/1... Discriminator Loss: 0.6684... Generator Loss: 1.8585
Epoch 1/1... Discriminator Loss: 0.5928... Generator Loss: 1.7188
Epoch 1/1... Discriminator Loss: 0.5258... Generator Loss: 1.9998
Epoch 1/1... Discriminator Loss: 0.6636... Generator Loss: 2.5869
Epoch 1/1... Discriminator Loss: 0.6559... Generator Loss: 2.6114
Epoch 1/1... Discriminator Loss: 0.5108... Generator Loss: 2.3700
Epoch 1/1... Discriminator Loss: 0.8101... Generator Loss: 1.0727
Epoch 1/1... Discriminator Loss: 0.7729... Generator Loss: 2.7940
Epoch 1/1... Discriminator Loss: 0.5637... Generator Loss: 2.7497
Epoch 1/1... Discriminator Loss: 0.7162... Generator Loss: 1.6124
Epoch 1/1... Discriminator Loss: 1.4442... Generator Loss: 0.6316
Epoch 1/1... Discriminator Loss: 0.5938... Generator Loss: 1.9216
Epoch 1/1... Discriminator Loss: 0.6240... Generator Loss: 1.5012
Epoch 1/1... Discriminator Loss: 0.9235... Generator Loss: 1.2170
Epoch 1/1... Discriminator Loss: 0.6768... Generator Loss: 1.9523
Epoch 1/1... Discriminator Loss: 0.5890... Generator Loss: 2.0468
Epoch 1/1... Discriminator Loss: 0.5604... Generator Loss: 2.2860
Epoch 1/1... Discriminator Loss: 0.7090... Generator Loss: 1.3835
Epoch 1/1... Discriminator Loss: 0.6180... Generator Loss: 2.1367
Epoch 1/1... Discriminator Loss: 0.5865... Generator Loss: 1.8336
Epoch 1/1... Discriminator Loss: 0.8471... Generator Loss: 1.3996
Epoch 1/1... Discriminator Loss: 0.8027... Generator Loss: 1.2748
Epoch 1/1... Discriminator Loss: 0.5676... Generator Loss: 2.2303
Epoch 1/1... Discriminator Loss: 0.6002... Generator Loss: 3.0030
Epoch 1/1... Discriminator Loss: 0.5706... Generator Loss: 1.9917
Epoch 1/1... Discriminator Loss: 0.5512... Generator Loss: 2.1370
Epoch 1/1... Discriminator Loss: 0.8476... Generator Loss: 3.3237
Epoch 1/1... Discriminator Loss: 0.6975... Generator Loss: 1.8095
Epoch 1/1... Discriminator Loss: 0.6609... Generator Loss: 1.6669
Epoch 1/1... Discriminator Loss: 0.5986... Generator Loss: 1.9151
Epoch 1/1... Discriminator Loss: 0.6979... Generator Loss: 2.5265
Epoch 1/1... Discriminator Loss: 0.6201... Generator Loss: 2.3651
Epoch 1/1... Discriminator Loss: 0.8159... Generator Loss: 1.4931
Epoch 1/1... Discriminator Loss: 0.5333... Generator Loss: 1.9567
Epoch 1/1... Discriminator Loss: 0.6407... Generator Loss: 1.8854
Epoch 1/1... Discriminator Loss: 0.5682... Generator Loss: 2.2695
Epoch 1/1... Discriminator Loss: 0.5088... Generator Loss: 2.3701
Epoch 1/1... Discriminator Loss: 0.5211... Generator Loss: 2.6907
Epoch 1/1... Discriminator Loss: 0.6430... Generator Loss: 1.3013
Epoch 1/1... Discriminator Loss: 0.6681... Generator Loss: 2.2583
Epoch 1/1... Discriminator Loss: 1.0181... Generator Loss: 1.0662
Epoch 1/1... Discriminator Loss: 0.5972... Generator Loss: 2.0557
Epoch 1/1... Discriminator Loss: 0.6666... Generator Loss: 1.6881
Epoch 1/1... Discriminator Loss: 0.7382... Generator Loss: 1.4383
Epoch 1/1... Discriminator Loss: 1.2831... Generator Loss: 0.9159
Epoch 1/1... Discriminator Loss: 0.5346... Generator Loss: 2.3338
Epoch 1/1... Discriminator Loss: 0.5769... Generator Loss: 2.2777
Epoch 1/1... Discriminator Loss: 0.8612... Generator Loss: 1.3786
Epoch 1/1... Discriminator Loss: 1.0086... Generator Loss: 1.0873
Epoch 1/1... Discriminator Loss: 0.5970... Generator Loss: 2.4906
Epoch 1/1... Discriminator Loss: 0.6470... Generator Loss: 2.0011
Epoch 1/1... Discriminator Loss: 0.8912... Generator Loss: 1.4344
Epoch 1/1... Discriminator Loss: 0.6477... Generator Loss: 1.7058
Epoch 1/1... Discriminator Loss: 0.5900... Generator Loss: 1.9039
Epoch 1/1... Discriminator Loss: 0.6247... Generator Loss: 1.5412
Epoch 1/1... Discriminator Loss: 0.6495... Generator Loss: 1.8004
Epoch 1/1... Discriminator Loss: 0.7040... Generator Loss: 1.4472
Epoch 1/1... Discriminator Loss: 0.5770... Generator Loss: 2.3127
Epoch 1/1... Discriminator Loss: 0.7042... Generator Loss: 1.3795
Epoch 1/1... Discriminator Loss: 0.5108... Generator Loss: 2.0891
Epoch 1/1... Discriminator Loss: 0.6930... Generator Loss: 1.4744
Epoch 1/1... Discriminator Loss: 0.5326... Generator Loss: 2.1098
Epoch 1/1... Discriminator Loss: 0.5427... Generator Loss: 2.2775
Epoch 1/1... Discriminator Loss: 0.7410... Generator Loss: 1.7228
Epoch 1/1... Discriminator Loss: 0.6874... Generator Loss: 1.7727
Epoch 1/1... Discriminator Loss: 0.5450... Generator Loss: 2.4985
Epoch 1/1... Discriminator Loss: 0.6270... Generator Loss: 1.8426
Epoch 1/1... Discriminator Loss: 0.6003... Generator Loss: 1.8973
Epoch 1/1... Discriminator Loss: 0.5925... Generator Loss: 2.0940
Epoch 1/1... Discriminator Loss: 0.7559... Generator Loss: 1.2500
Epoch 1/1... Discriminator Loss: 0.7386... Generator Loss: 1.2180
Epoch 1/1... Discriminator Loss: 0.5502... Generator Loss: 1.9447
Epoch 1/1... Discriminator Loss: 0.5667... Generator Loss: 1.7772
Epoch 1/1... Discriminator Loss: 0.7261... Generator Loss: 2.4608
Epoch 1/1... Discriminator Loss: 0.6495... Generator Loss: 1.4527
Epoch 1/1... Discriminator Loss: 0.7452... Generator Loss: 1.3646
Epoch 1/1... Discriminator Loss: 0.5005... Generator Loss: 2.7237
Epoch 1/1... Discriminator Loss: 0.5615... Generator Loss: 2.1278
Epoch 1/1... Discriminator Loss: 0.5728... Generator Loss: 2.3395
Epoch 1/1... Discriminator Loss: 0.7629... Generator Loss: 1.3596
Epoch 1/1... Discriminator Loss: 0.7058... Generator Loss: 1.6602
Epoch 1/1... Discriminator Loss: 0.6158... Generator Loss: 1.8091
Epoch 1/1... Discriminator Loss: 0.5908... Generator Loss: 2.0616
Epoch 1/1... Discriminator Loss: 0.5794... Generator Loss: 1.6275
Epoch 1/1... Discriminator Loss: 0.5931... Generator Loss: 2.2940
Epoch 1/1... Discriminator Loss: 0.6795... Generator Loss: 1.5908
Epoch 1/1... Discriminator Loss: 0.7196... Generator Loss: 2.0756
Epoch 1/1... Discriminator Loss: 0.6473... Generator Loss: 2.2286
Epoch 1/1... Discriminator Loss: 0.5944... Generator Loss: 1.6724
Epoch 1/1... Discriminator Loss: 0.5095... Generator Loss: 1.8219
Epoch 1/1... Discriminator Loss: 0.7060... Generator Loss: 1.4314
Epoch 1/1... Discriminator Loss: 0.5430... Generator Loss: 2.7158
Epoch 1/1... Discriminator Loss: 0.5461... Generator Loss: 2.1424
Epoch 1/1... Discriminator Loss: 0.5356... Generator Loss: 1.6846
Epoch 1/1... Discriminator Loss: 0.6232... Generator Loss: 1.6596
Epoch 1/1... Discriminator Loss: 0.8466... Generator Loss: 1.2248
Epoch 1/1... Discriminator Loss: 0.6025... Generator Loss: 2.2476
Epoch 1/1... Discriminator Loss: 0.5156... Generator Loss: 2.2046
Epoch 1/1... Discriminator Loss: 0.7169... Generator Loss: 1.0545
Epoch 1/1... Discriminator Loss: 0.7394... Generator Loss: 2.7478
Epoch 1/1... Discriminator Loss: 0.5722... Generator Loss: 2.7374
Epoch 1/1... Discriminator Loss: 0.5718... Generator Loss: 3.2093
Epoch 1/1... Discriminator Loss: 0.4841... Generator Loss: 2.3907
Epoch 1/1... Discriminator Loss: 0.8830... Generator Loss: 1.0784
Epoch 1/1... Discriminator Loss: 0.6181... Generator Loss: 1.7237
Epoch 1/1... Discriminator Loss: 0.7127... Generator Loss: 1.7418
Epoch 1/1... Discriminator Loss: 0.8684... Generator Loss: 1.1945
Epoch 1/1... Discriminator Loss: 0.6336... Generator Loss: 1.6463
Epoch 1/1... Discriminator Loss: 0.5562... Generator Loss: 1.8216
Epoch 1/1... Discriminator Loss: 0.8329... Generator Loss: 1.2229
Epoch 1/1... Discriminator Loss: 0.5919... Generator Loss: 1.9938
Epoch 1/1... Discriminator Loss: 0.6634... Generator Loss: 2.2143
Epoch 1/1... Discriminator Loss: 0.6080... Generator Loss: 1.9974
Epoch 1/1... Discriminator Loss: 0.5748... Generator Loss: 2.3268
Epoch 1/1... Discriminator Loss: 0.7179... Generator Loss: 1.4117
Epoch 1/1... Discriminator Loss: 0.7455... Generator Loss: 1.3382
Epoch 1/1... Discriminator Loss: 0.6438... Generator Loss: 1.8816
Epoch 1/1... Discriminator Loss: 0.5986... Generator Loss: 1.8484
Epoch 1/1... Discriminator Loss: 0.7417... Generator Loss: 2.5645
Epoch 1/1... Discriminator Loss: 0.7683... Generator Loss: 1.4657
Epoch 1/1... Discriminator Loss: 0.5814... Generator Loss: 2.2009
Epoch 1/1... Discriminator Loss: 0.6444... Generator Loss: 2.3133
Epoch 1/1... Discriminator Loss: 0.4988... Generator Loss: 2.7400
Epoch 1/1... Discriminator Loss: 0.6809... Generator Loss: 1.6696
Epoch 1/1... Discriminator Loss: 0.6938... Generator Loss: 1.4742
Epoch 1/1... Discriminator Loss: 0.7707... Generator Loss: 1.2521
Epoch 1/1... Discriminator Loss: 0.7801... Generator Loss: 1.2735
Epoch 1/1... Discriminator Loss: 0.7359... Generator Loss: 1.5525
Epoch 1/1... Discriminator Loss: 0.6916... Generator Loss: 1.5059
Epoch 1/1... Discriminator Loss: 0.8100... Generator Loss: 1.2372
Epoch 1/1... Discriminator Loss: 0.5837... Generator Loss: 2.5717
Epoch 1/1... Discriminator Loss: 0.5714... Generator Loss: 2.5410
Epoch 1/1... Discriminator Loss: 0.6811... Generator Loss: 1.5600
Epoch 1/1... Discriminator Loss: 0.6756... Generator Loss: 2.1021
Epoch 1/1... Discriminator Loss: 0.7191... Generator Loss: 1.1828
Epoch 1/1... Discriminator Loss: 0.5678... Generator Loss: 2.2975
Epoch 1/1... Discriminator Loss: 0.5775... Generator Loss: 2.0921
Epoch 1/1... Discriminator Loss: 0.5932... Generator Loss: 2.7419
Epoch 1/1... Discriminator Loss: 0.7173... Generator Loss: 1.4050
Epoch 1/1... Discriminator Loss: 0.6095... Generator Loss: 2.2644
Epoch 1/1... Discriminator Loss: 0.6045... Generator Loss: 1.6335
Epoch 1/1... Discriminator Loss: 0.5860... Generator Loss: 2.3687
Epoch 1/1... Discriminator Loss: 0.8409... Generator Loss: 1.5014
Epoch 1/1... Discriminator Loss: 0.6642... Generator Loss: 2.0515
Epoch 1/1... Discriminator Loss: 0.7977... Generator Loss: 1.3734
Epoch 1/1... Discriminator Loss: 0.5824... Generator Loss: 2.4125
Epoch 1/1... Discriminator Loss: 0.7217... Generator Loss: 1.3731
Epoch 1/1... Discriminator Loss: 0.6507... Generator Loss: 2.1665
Epoch 1/1... Discriminator Loss: 0.6169... Generator Loss: 1.8642
Epoch 1/1... Discriminator Loss: 0.6334... Generator Loss: 1.7549
Epoch 1/1... Discriminator Loss: 0.7812... Generator Loss: 1.4026
Epoch 1/1... Discriminator Loss: 0.5976... Generator Loss: 1.9126
Epoch 1/1... Discriminator Loss: 0.6177... Generator Loss: 1.7905
Epoch 1/1... Discriminator Loss: 0.5408... Generator Loss: 2.1333
Epoch 1/1... Discriminator Loss: 0.6952... Generator Loss: 2.7264
Epoch 1/1... Discriminator Loss: 0.6284... Generator Loss: 2.0027
Epoch 1/1... Discriminator Loss: 0.5609... Generator Loss: 2.6439
Epoch 1/1... Discriminator Loss: 0.5527... Generator Loss: 1.6021
Epoch 1/1... Discriminator Loss: 0.6230... Generator Loss: 1.8941
Epoch 1/1... Discriminator Loss: 0.6059... Generator Loss: 1.6593
Epoch 1/1... Discriminator Loss: 0.5902... Generator Loss: 1.5428
Epoch 1/1... Discriminator Loss: 0.5741... Generator Loss: 1.9436
Epoch 1/1... Discriminator Loss: 0.9602... Generator Loss: 1.1743
Epoch 1/1... Discriminator Loss: 0.8163... Generator Loss: 1.1391
Epoch 1/1... Discriminator Loss: 0.5044... Generator Loss: 2.1831
Epoch 1/1... Discriminator Loss: 1.1104... Generator Loss: 0.8371
Epoch 1/1... Discriminator Loss: 0.6076... Generator Loss: 1.9657
Epoch 1/1... Discriminator Loss: 0.6454... Generator Loss: 2.3419
Epoch 1/1... Discriminator Loss: 0.7276... Generator Loss: 1.6755
Epoch 1/1... Discriminator Loss: 0.7255... Generator Loss: 1.4522
Epoch 1/1... Discriminator Loss: 0.6711... Generator Loss: 1.6995
Epoch 1/1... Discriminator Loss: 0.6238... Generator Loss: 2.3686
Epoch 1/1... Discriminator Loss: 0.6141... Generator Loss: 2.0233
Epoch 1/1... Discriminator Loss: 0.6325... Generator Loss: 1.4141
Epoch 1/1... Discriminator Loss: 0.6264... Generator Loss: 2.2516
Epoch 1/1... Discriminator Loss: 0.5931... Generator Loss: 2.0105
Epoch 1/1... Discriminator Loss: 0.6763... Generator Loss: 1.4197
Epoch 1/1... Discriminator Loss: 0.5868... Generator Loss: 1.6100
Epoch 1/1... Discriminator Loss: 0.6451... Generator Loss: 1.4087
Epoch 1/1... Discriminator Loss: 0.6557... Generator Loss: 2.0801
Epoch 1/1... Discriminator Loss: 0.7426... Generator Loss: 1.7234
Epoch 1/1... Discriminator Loss: 0.6707... Generator Loss: 1.3367
Epoch 1/1... Discriminator Loss: 0.7302... Generator Loss: 2.1775
Epoch 1/1... Discriminator Loss: 0.6428... Generator Loss: 1.8782
Epoch 1/1... Discriminator Loss: 0.6090... Generator Loss: 1.5575
Epoch 1/1... Discriminator Loss: 0.7488... Generator Loss: 1.4594
Epoch 1/1... Discriminator Loss: 0.6118... Generator Loss: 1.8933
Epoch 1/1... Discriminator Loss: 0.6172... Generator Loss: 1.7347
Epoch 1/1... Discriminator Loss: 0.5968... Generator Loss: 1.9804
Epoch 1/1... Discriminator Loss: 0.6029... Generator Loss: 2.3128
Epoch 1/1... Discriminator Loss: 0.8292... Generator Loss: 1.2207
Epoch 1/1... Discriminator Loss: 0.5946... Generator Loss: 1.8413
Epoch 1/1... Discriminator Loss: 0.6424... Generator Loss: 2.0569
Epoch 1/1... Discriminator Loss: 0.5796... Generator Loss: 2.0929
Epoch 1/1... Discriminator Loss: 0.6180... Generator Loss: 3.0159
Epoch 1/1... Discriminator Loss: 0.5662... Generator Loss: 1.9866
Epoch 1/1... Discriminator Loss: 0.7292... Generator Loss: 1.5039
Epoch 1/1... Discriminator Loss: 0.5292... Generator Loss: 2.4823
Epoch 1/1... Discriminator Loss: 0.4860... Generator Loss: 2.1219
Epoch 1/1... Discriminator Loss: 0.4941... Generator Loss: 2.5865
Epoch 1/1... Discriminator Loss: 0.7047... Generator Loss: 1.9342
Epoch 1/1... Discriminator Loss: 0.6083... Generator Loss: 2.0442
Epoch 1/1... Discriminator Loss: 0.4728... Generator Loss: 2.9370
Epoch 1/1... Discriminator Loss: 0.9451... Generator Loss: 1.5374
Epoch 1/1... Discriminator Loss: 0.4703... Generator Loss: 2.9680
Epoch 1/1... Discriminator Loss: 0.5419... Generator Loss: 1.8548
Epoch 1/1... Discriminator Loss: 0.5540... Generator Loss: 1.8068
Epoch 1/1... Discriminator Loss: 0.7176... Generator Loss: 1.5275
Epoch 1/1... Discriminator Loss: 0.5297... Generator Loss: 2.0604
Epoch 1/1... Discriminator Loss: 0.6208... Generator Loss: 2.3882
Epoch 1/1... Discriminator Loss: 0.5049... Generator Loss: 2.7647
Epoch 1/1... Discriminator Loss: 0.6056... Generator Loss: 2.0219
Epoch 1/1... Discriminator Loss: 0.6000... Generator Loss: 2.1407
Epoch 1/1... Discriminator Loss: 0.7188... Generator Loss: 1.5121
Epoch 1/1... Discriminator Loss: 0.6951... Generator Loss: 2.2498
Epoch 1/1... Discriminator Loss: 0.6370... Generator Loss: 2.5431
Epoch 1/1... Discriminator Loss: 0.8117... Generator Loss: 0.9858
Epoch 1/1... Discriminator Loss: 0.5707... Generator Loss: 2.0954
Epoch 1/1... Discriminator Loss: 0.6291... Generator Loss: 1.9534
Epoch 1/1... Discriminator Loss: 0.6412... Generator Loss: 1.7519
Epoch 1/1... Discriminator Loss: 0.6518... Generator Loss: 1.3431
Epoch 1/1... Discriminator Loss: 0.7596... Generator Loss: 1.2063
Epoch 1/1... Discriminator Loss: 0.6012... Generator Loss: 1.7675
Epoch 1/1... Discriminator Loss: 0.5366... Generator Loss: 2.1373
Epoch 1/1... Discriminator Loss: 0.5155... Generator Loss: 2.4152
Epoch 1/1... Discriminator Loss: 0.5905... Generator Loss: 2.0629
Epoch 1/1... Discriminator Loss: 0.7606... Generator Loss: 1.4440
Epoch 1/1... Discriminator Loss: 0.6888... Generator Loss: 1.5842
Epoch 1/1... Discriminator Loss: 0.9431... Generator Loss: 2.6779
Epoch 1/1... Discriminator Loss: 0.8142... Generator Loss: 1.2742
Epoch 1/1... Discriminator Loss: 0.6379... Generator Loss: 2.2643
Epoch 1/1... Discriminator Loss: 0.8899... Generator Loss: 1.2063
Epoch 1/1... Discriminator Loss: 0.6809... Generator Loss: 2.5052
Epoch 1/1... Discriminator Loss: 0.7268... Generator Loss: 2.3675
Epoch 1/1... Discriminator Loss: 0.7978... Generator Loss: 1.3912
Epoch 1/1... Discriminator Loss: 0.5435... Generator Loss: 2.0893
Epoch 1/1... Discriminator Loss: 0.6069... Generator Loss: 1.5779
Epoch 1/1... Discriminator Loss: 0.5887... Generator Loss: 1.7332
Epoch 1/1... Discriminator Loss: 0.6019... Generator Loss: 2.3778
Epoch 1/1... Discriminator Loss: 0.5726... Generator Loss: 2.4222
Epoch 1/1... Discriminator Loss: 0.7396... Generator Loss: 1.4732
Epoch 1/1... Discriminator Loss: 0.7684... Generator Loss: 1.4663
Epoch 1/1... Discriminator Loss: 0.7454... Generator Loss: 1.6284
Epoch 1/1... Discriminator Loss: 0.9596... Generator Loss: 1.0216
Epoch 1/1... Discriminator Loss: 0.7572... Generator Loss: 2.2123
Epoch 1/1... Discriminator Loss: 0.5809... Generator Loss: 2.0620
Epoch 1/1... Discriminator Loss: 0.6094... Generator Loss: 1.6232
Epoch 1/1... Discriminator Loss: 0.6468... Generator Loss: 1.7452
Epoch 1/1... Discriminator Loss: 0.6292... Generator Loss: 1.5414
Epoch 1/1... Discriminator Loss: 0.8494... Generator Loss: 0.9571
Epoch 1/1... Discriminator Loss: 0.5514... Generator Loss: 1.7730
Epoch 1/1... Discriminator Loss: 0.8758... Generator Loss: 1.0501
Epoch 1/1... Discriminator Loss: 0.5371... Generator Loss: 2.1155
Epoch 1/1... Discriminator Loss: 0.5553... Generator Loss: 2.7769
Epoch 1/1... Discriminator Loss: 0.6132... Generator Loss: 1.3570
Epoch 1/1... Discriminator Loss: 0.5690... Generator Loss: 2.8584
Epoch 1/1... Discriminator Loss: 0.6511... Generator Loss: 1.7825
Epoch 1/1... Discriminator Loss: 0.6329... Generator Loss: 1.6879
Epoch 1/1... Discriminator Loss: 0.5890... Generator Loss: 1.7048
Epoch 1/1... Discriminator Loss: 0.6907... Generator Loss: 1.4335
Epoch 1/1... Discriminator Loss: 0.5238... Generator Loss: 1.9747
Epoch 1/1... Discriminator Loss: 0.4677... Generator Loss: 2.0667
Epoch 1/1... Discriminator Loss: 0.6005... Generator Loss: 2.1161
Epoch 1/1... Discriminator Loss: 0.6136... Generator Loss: 1.7330
Epoch 1/1... Discriminator Loss: 0.8069... Generator Loss: 1.3770
Epoch 1/1... Discriminator Loss: 0.5624... Generator Loss: 2.0565
Epoch 1/1... Discriminator Loss: 0.6333... Generator Loss: 1.9585
Epoch 1/1... Discriminator Loss: 0.7815... Generator Loss: 1.2086
Epoch 1/1... Discriminator Loss: 0.8389... Generator Loss: 1.2777
Epoch 1/1... Discriminator Loss: 0.8763... Generator Loss: 1.0825
Epoch 1/1... Discriminator Loss: 0.5949... Generator Loss: 1.7254
Epoch 1/1... Discriminator Loss: 0.5523... Generator Loss: 1.8818
Epoch 1/1... Discriminator Loss: 0.6781... Generator Loss: 1.3956
Epoch 1/1... Discriminator Loss: 0.6836... Generator Loss: 1.6274
Epoch 1/1... Discriminator Loss: 0.6892... Generator Loss: 3.0555
Epoch 1/1... Discriminator Loss: 0.4820... Generator Loss: 3.3743
Epoch 1/1... Discriminator Loss: 0.7185... Generator Loss: 1.6537
Epoch 1/1... Discriminator Loss: 0.5663... Generator Loss: 2.0147
Epoch 1/1... Discriminator Loss: 0.6956... Generator Loss: 1.6545
Epoch 1/1... Discriminator Loss: 0.5333... Generator Loss: 2.4276
Epoch 1/1... Discriminator Loss: 0.7686... Generator Loss: 1.5125
Epoch 1/1... Discriminator Loss: 0.7106... Generator Loss: 1.8249
Epoch 1/1... Discriminator Loss: 0.6334... Generator Loss: 1.8207
Epoch 1/1... Discriminator Loss: 0.6001... Generator Loss: 1.6353
Epoch 1/1... Discriminator Loss: 0.5366... Generator Loss: 2.1099
Epoch 1/1... Discriminator Loss: 0.6902... Generator Loss: 1.4016
Epoch 1/1... Discriminator Loss: 0.6663... Generator Loss: 1.7296
Epoch 1/1... Discriminator Loss: 0.6102... Generator Loss: 1.8255
Epoch 1/1... Discriminator Loss: 0.6495... Generator Loss: 1.5378
Epoch 1/1... Discriminator Loss: 0.7022... Generator Loss: 1.5467
Epoch 1/1... Discriminator Loss: 0.6661... Generator Loss: 1.6483
Epoch 1/1... Discriminator Loss: 0.5801... Generator Loss: 1.9463
Epoch 1/1... Discriminator Loss: 0.5698... Generator Loss: 1.7988
Epoch 1/1... Discriminator Loss: 0.6862... Generator Loss: 2.2889
Epoch 1/1... Discriminator Loss: 0.5752... Generator Loss: 1.9376
Epoch 1/1... Discriminator Loss: 0.6834... Generator Loss: 1.6213
Epoch 1/1... Discriminator Loss: 1.3774... Generator Loss: 0.9420
Epoch 1/1... Discriminator Loss: 0.5704... Generator Loss: 2.1431
Epoch 1/1... Discriminator Loss: 0.6168... Generator Loss: 2.2116
Epoch 1/1... Discriminator Loss: 0.7636... Generator Loss: 1.2141

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.